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Syed Moiz Ali

ML Engineer & Infrastructure Architect

Production-proven ML Engineer with 9 years building scalable infrastructure and backend systems for AI-driven applications. Expert in cloud architecture, MLOps, and delivering measurable business impact.

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About

Production-proven ML Engineer with 9 years of expertise in scalable infrastructure and AI systems. Delivered 92% accuracy emotion detection, 40% user engagement boost through recommender systems, and 60% faster deployments. IIT Kanpur graduate with 8 elite certifications and 4 research publications.

Core Expertise

Cloud & Infrastructure
AWS Terraform Kubernetes Docker
Backend & Data
Python Node.js TypeScript PostgreSQL Redis
ML & AI
PyTorch TensorFlow MLflow Spark

Experience

  • Infrastructure Engineering: Designed IaC-driven AWS architecture with Terraform, reducing deployment cycles by 60% while maintaining zero-downtime deployments across multi-region production environments.
  • Backend Architecture: Built microservices handling 3x traffic spikes using FastAPI and Node.js, with PostgreSQL/MongoDB backends optimized for sub-100ms query latency at scale.
  • Platform Automation: Implemented end-to-end CI/CD with GitHub Actions and K8s orchestration, achieving 85% reduction in deployment failures and enabling daily releases.
  • Observability: Deployed comprehensive monitoring stack (Prometheus/Grafana) with custom metrics pipelines, reducing MTTR by 70% and enabling proactive incident prevention.

  • Database Optimization: Re-architected PostgreSQL/NoSQL schemas with advanced indexing and partitioning strategies, achieving 32% query performance improvement across distributed data stores.
  • ETL Pipelines: Built automated data processing systems with real-time monitoring and self-healing capabilities, eliminating manual interventions and ensuring data consistency.

Project Details

Built high-throughput NLP pipeline processing millions of documents using distributed tokenization, NER, and sentiment analysis. Dockerized architecture enabled rapid model iteration and reduced processing time by 70%.

Developed real-time anomaly detection system using autoencoder-LSTM hybrid architecture with Isolation Forest ensemble, achieving 95%+ accuracy on high-frequency streaming data.

Implemented U-Net architecture for medical image segmentation, validated with Cellpose, achieving clinical-grade accuracy across diverse imaging modalities.

Architected multi-modal fusion system combining computer vision and sensor streams for autonomous vehicle perception with real-time decision-making capabilities.

Fine-tuned domain-specific BERT variants for sentiment analysis, outperforming baseline models by 15% on multi-domain benchmarks.

Education

  • Institute: Indian Institute of Technology, Kanpur
  • Department: Department of Management Sciences
  • Major: Production & Operations Management
  • CGPA: 8.0
  • Year: 2009-2011
  • Location: Kanpur, Uttar Pradesh, India

  • Institute: RITEE, CSVTU
  • Department: Electronics & Telecommunication Engineering
  • Major Project: Implementation of Image Processing and Image Enhancement using MATLAB.
  • CGPA: 8.5
  • Year: 2005-2009
  • Location: Raipur, Chhattisgarh, India

Certifications

  • Date of Completion: June 2021
  • Certification Provider: Stanford Online
  • View Certification

  • Date of Completion: April 2021
  • Certification Provider: DeepLearning.ai
  • View Certification

  • Date of Completion: November 2022
  • Certification Provider: DeepLearning.ai
  • View Certification

Research Experience & Publications

  • Institution: Sultan Qaboos University
  • Year: 2014-2015
  • Undertook a project titled: "Mediator-based order acceptance decision system under the make-to-order company."
  • Worked under the guidance of Dr. Sujan Piya, focusing on improving order acceptance mechanisms.

  • Company: Central UP Gas Limited
  • Year: 2010
  • Market Analysis: Led an initiative to examine the market potential for natural gas in the Rania & Jainpur Industrial Areas.
  • Formulated strategies to establish Piped Natural Gas service stations, enhancing the distribution network.

  • Sharma, R. R. K., & Ali, S. M. (2017). Reducing a Lot Sizing Problem with Set up, Production, Shortage and Inventory Costs to Lot Sizing Problem with Set up, Production and Inventory Costs. American Journal of Operations Research, 7, 282-284. Link
  • Ali, S. M., Sharma, R.R.K., & Gupta, O.K. (2015). Lagrangian Relaxation Procedure for the Capacitated Dynamic Lot Sizing Problem. AIMS International Conference on Management. Link
  • Syed, M. A., & Sharif. (2012). Aggregate Planning for Semi-finished goods under make-to-stock environment. International Journal of Advances in Management, Technology & Engineering Sciences, 1(8(I)), 104-107.
  • Sharif, & Syed, M.A. (2012). Procurement Policies & Inventory Management System in Manufacturing and Service Settings: An Optimization Framework. International Journal of Business, Management & Social Sciences, 1(9), 27-32.
Send an email to communicate.

Get in Touch

Syed Moiz Ali

9984673534

moizeali@gmail.com

Hyderabad, India

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